Position Overview
QpiVolta Technologies is seeking a Research Engineer to work on accelerating battery modeling
through advanced Machine Learning techniques. The ideal candidate will have a strong background in both computational chemistry and machine learning, with experience in
multi-scale modeling of materials and interfaces.
Key Responsibilities
● Develop and implement machine learning models for battery material interface and
transport phenomena.
● Integrate multi-scale modeling approaches spanning quantum chemistry, molecular
dynamics, and continuum models.
● Apply and fine-tune ML force fields for accurate materials simulation.
● Contribute to the development of battery design and optimization workflows.
● Collaborate with interdisciplinary teams on battery modeling projects.
Required Qualifications
● Master’s degree in Chemistry, Materials Science, Chemical Engineering, Mathematics,
Physics, or a related field.
● Experience applying Large Language Models in Scientific Domains
● Solid background in computational modeling at multiple scales:
○ Density Functional Theory (DFT)
○ Molecular Dynamics (MD)
○ Coarse-grained modeling
○ Continuum modeling
● Experience with relevant software tools:
○ LAMMPS for molecular dynamics simulations
○ DFT software packages (e.g., VASP, Quantum ESPRESSO, or similar)
○ PyBaMM, Battery Design Studio (Python Battery Mathematical Modelling)
○ Battery design software tools
Technical Skills
● Demonstrated experience in:
○ Machine learning model development and implementation
○ Force field development and fine-tuning
○ Integration of multi-scale modeling approaches
○ Python programming and scientific computing libraries
○ Version control systems (e.g., Git)
Battery Modeling Workflow Experience
● Proficiency in electrochemical modeling workflows:
○ P2D (pseudo-two-dimensional) models for cell-level simulation
○ SPM (single particle model) for simplified cell analysis
○ Newman model implementati
📌 Research Engineer (Bengaluru)
🏢 QpiVolta Technologies
📍 Bengaluru
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